{ "data": { "a": { "slug": "jina-embeddings", "name": "Jina Embeddings and Reranker", "vendor": "Jina AI (Elastic)", "vendorUrl": "https://jina.ai", "kind": "http-api", "category": "embeddings", "summary": "jina-embeddings-v5 in text and omni (text, image, audio, video, PDF) variants at up to 32,768 tokens, plus the jina-reranker-v3.5 at 131,072 tokens a call.", "url": "https://www.anchorterminal.com/tools/jina-embeddings", "markdownUrl": "https://www.anchorterminal.com/tools/jina-embeddings.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/jina-embeddings.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/jina-embeddings.json", "repo": "https://github.com/jina-ai/MCP", "license": "Apache-2.0 (MCP server)", "transports": [ "http", "streamable-http" ], "remoteUrl": "https://api.jina.ai/v1/embeddings", "packages": [], "auth": "api-key", "authNotes": "`Authorization: Bearer` with a `jina_...` key. A new account gets a key with free tokens, and the same key works for Reader, Search, Embeddings, Reranker and the MCP server.", "pricing": "freemium", "pricingNotes": "Prepaid tokens, topped up through Stripe (cards, Google Pay, PayPal) and shared across every Jina API. A new key comes with free tokens. Non-text inputs are converted to tokens by the encoder, about 363 tokens an image on v5-omni, 4,840 on v4 and 16,000 on jina-clip-v2. Jina changed its pricing model on 2025-05-06, and the public pages don't state a US dollar price per token, so we don't list one (https://jina.ai/embeddings/).", "priceSummary": "Freemium", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": 12, "popularity": { "githubStars": 841, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-09-30" }, "docsUrl": "https://jina.ai/embeddings/", "llmsTxt": "https://jina.ai/models/llms.txt", "openapi": "https://api.jina.ai/openapi.json", "capabilities": [ "embed.text", "embed.multimodal", "embed.code", "embed.multilingual", "rerank" ], "tags": [ "hosted", "freemium", "free-tier", "no-card", "mcp", "prepaid", "eu" ], "lastRelease": "2026-09-18", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 438, "ranked": true, "rankOf": 842, "categoryRank": 5, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 86, "maintenance": 62, "payments": 30, "reliability": 65, "schema": 84, "security": 35, "transparency": 58 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "jina-reranker-v3.5 (20 July 2026) with a 131,072-token window and no document cap. No price per token in any currency on the public pages.", "bestFor": "Reranking large candidate sets and multimodal corpora with audio or video.", "strengths": [ "jina-reranker-v3.5 (20 July 2026) with a 131,072-token window and no document cap", "v5-omni embeds text, images, audio, video and PDFs into one space", "OpenAPI 3.1 file with enums for model, task and embedding_type, and error responses from 400 to 504", "Hosted MCP server with rerank and dedupe tools, filterable per client", "Doesn't train on inputs, per the terms" ], "weaknesses": [ "No price per token in any currency on the public pages", "One prepaid balance shared with Reader and Search, so a scraping job can drain the embedding budget", "26 automated incidents on the status feed from 15 September to 1 October 2026, and no status component for v5-omni or reranker v3.5", "No security.txt, no SLA and no API changelog", "The MCP server has no CI or tests, and current weights are CC BY-NC 4.0" ], "agentNotes": [ "Send the whole candidate set to rerank in one call. The 131K window on v3.5 fits hundreds of chunks", "On a 429, back off exponentially. Limits count per key when a key is sent, per IP otherwise", "Use /v1/batch/embeddings for large corpora rather than a loop of synchronous calls", "Add include_tags=rerank on the MCP URL to load only sort_by_relevance and deduplicate_strings", "Count image tokens before a big multimodal job, about 363 an image on v5-omni" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "ergonomics": 86, "maintenance": 62, "payments": 30, "reliability": 65, "schema": 84, "security": 35, "transparency": 45 }, "provenanceScore": 70 }, "connect": { "http": "curl https://api.jina.ai/v1/rerank \\\n -H \"Authorization: Bearer $JINA_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"model\":\"jina-reranker-v3.5\",\"query\":\"embedding price per million tokens\",\"documents\":[\"Tokens are prepaid and shared across APIs.\",\"Berlin is in Germany.\"],\"top_n\":1}'", "claudeCode": "claude mcp add --transport http jina \"https://mcp.jina.ai/v1?include_tags=rerank\" --header \"Authorization: Bearer $JINA_API_KEY\"", "config": { "mcpServers": { "jina": { "headers": { "Authorization": "Bearer ${JINA_API_KEY}" }, "url": "https://mcp.jina.ai/v1?include_tags=rerank" } } } }, "letme": { "capability": "https://letme.dev/embed.text", "tool": "https://letme.dev/jina-embeddings" }, "sameCompany": [ "jina-reader" ], "area": "models", "provenance": { "legalEntity": "Jina AI GmbH", "domain": "jina.ai", "domainRegistered": "2020-01-20", "domainNote": "Jina AI GmbH is a subsidiary of Elastic N.V. since October 2025, and the privacy statement is Elastic's.", "endpointOnVendorDomain": true, "terms": "https://jina.ai/legal/", "privacy": "https://www.elastic.co/legal/privacy-statement", "statusPage": "https://status.jina.ai", "changelog": "", "securityTxt": "none", "checked": "2026-10-02", "notes": [ "The terms give Prinzessinnenstraße 19-20, 10969 Berlin, Germany, under German law with Berlin courts.", "jina.ai/.well-known/security.txt returns 404. The root jina.ai/llms.txt returns 404, but the embeddings page links llms.txt at jina.ai/models/llms.txt, an OpenAPI 3.1 document at api.jina.ai/openapi.json and API docs at api.jina.ai/scalar.", "The MCP server's source is public under Apache-2.0 (version 1.10.0, last commit 2026-09-18)." ], "score": 70 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/jina-embeddings.json", "live": { "slug": "jina-embeddings", "probe": { "target": "https://api.jina.ai/v1/embeddings", "method": "get", "lastAt": "2026-10-09T10:42:46.188660172Z", "lastOk": true, "lastStatus": 401, "lastMs": 186, "lastNote": "asks for credentials", "authRequired": true, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 192, "p95ms24h": 266, "samples24h": 260, "samples30d": 2098, "days": [ { "date": "2026-10-01", "probes": 109, "ok": 109 }, { "date": "2026-10-02", "probes": 248, "ok": 248 }, { "date": "2026-10-03", "probes": 271, "ok": 271 }, { "date": "2026-10-04", "probes": 272, "ok": 272 }, { "date": "2026-10-05", "probes": 272, "ok": 272 }, { "date": "2026-10-06", "probes": 272, "ok": 272 }, { "date": "2026-10-07", "probes": 272, "ok": 272 }, { "date": "2026-10-08", "probes": 268, "ok": 268 }, { "date": "2026-10-09", "probes": 114, "ok": 114 } ] }, "vendorStatus": { "page": "https://status.jina.ai", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-10-09T10:41:45.539354849Z" }, "githubStars": 875, "securityTxt": { "url": "https://jina.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-08T15:38:49.272613996Z" }, "llmsTxt": { "url": "https://jina.ai/models/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-08T14:00:33.128296706Z" }, "domain": { "domain": "jina.ai", "registered": "2020-01-20", "source": "https://rdap.identitydigital.services/rdap/domain/jina.ai", "checkedAt": "2026-10-04T13:08:08.912440143Z" }, "pages": [ { "url": "https://www.elastic.co/legal/privacy-statement", "kind": "privacy", "status": 200, "checkedAt": "2026-10-08T18:27:33.853078847Z", "changedAt": "2026-10-07T18:11:44.162717631Z", "fingerprint": "33a9d69f6773" }, { "url": "https://jina.ai/legal/", "kind": "terms", "status": 200, "checkedAt": "2026-10-08T18:21:02.830277731Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "822c862ff72d" } ], "updatedAt": "2026-10-09T10:42:46.188660172Z" } }, "answer": "Jina Embeddings and Reranker and NVIDIA NeMo Retriever Embedding and Reranking NIMs score within a point of each other on agent readiness, 61 (C) and 61 (C). NVIDIA NeMo Retriever Embedding and Reranking NIMs leads on security \u0026 auth, payments \u0026 pricing and transparency \u0026 trust.", "b": { "slug": "nvidia-nemo-retriever", "name": "NVIDIA NeMo Retriever Embedding and Reranking NIMs", "vendor": "NVIDIA", "vendorUrl": "https://www.nvidia.com", "kind": "http-api", "category": "embeddings", "summary": "NVIDIA's NeMo Retriever Embedding and Reranking NIMs are GPU containers that run text and image embedding models and rerankers behind a local REST API, with `/v1/embeddings` in the OpenAI shape and `/v1/ranking`.", "url": "https://www.anchorterminal.com/tools/nvidia-nemo-retriever", "markdownUrl": "https://www.anchorterminal.com/tools/nvidia-nemo-retriever.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nvidia-nemo-retriever.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nvidia-nemo-retriever.json", "license": "Proprietary containers under the NVIDIA Software Licence Agreement and Product-Specific Terms for AI Products. Models carry their own licences, such as OpenMDW 1.1 for `nvidia/nemotron-3-embed-1b` and the NVIDIA Open Model Licence for the Llama Nemotron models", "transports": [ "http" ], "packages": [ { "registry": "pypi", "name": "langchain-nvidia-ai-endpoints" } ], "auth": "none", "authNotes": "The NIM's own API takes no credential. NVIDIA's security page says the deployer must secure the endpoints and suggests a proxy with HTTPS. Pulling images from `nvcr.io` needs a personal NGC API key, created by a person at org.ngc.nvidia.com and sent as the password for the user `$oauthtoken`. Model weights download from Hugging Face by default with `HF_TOKEN`, or from NGC with `NGC_API_KEY`. Some models need their licence terms accepted on the NGC catalogue page first. TLS is built in through `NIM_SERVER_TLS_CERT_PATH` and `NIM_SERVER_TLS_KEY_PATH`.", "pricing": "freemium", "pricingNotes": "Free for research, development and testing on up to 16 GPUs through the NVIDIA developer programme, with no card. Production use needs NVIDIA AI Enterprise, listed at $4,500 a GPU a year through partners or $1 a GPU-hour on AWS, Azure, Google Cloud and Oracle marketplaces, plus the instance. A 90-day AI Enterprise trial licence is available on request. The hosted trial endpoints on build.nvidia.com are free for prototyping (https://docs.nvidia.com/ai-enterprise/planning-resource/licensing-guide/latest/pricing.html, checked 2026-10-08).", "priceSummary": "Freemium", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the NIM docs, the OpenAPI files or the AI Enterprise pricing page (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": 133630, "asOf": "2026-10-08" }, "docsUrl": "https://docs.nvidia.com/nim/nemo-retriever/embedding/latest/overview.html", "openapi": "https://docs.nvidia.com/nim/nemo-retriever/embedding/latest/_downloads/9957cfb9472fcf23c93820c1922c4343/openai-api.openapi.yaml", "capabilities": [ "embed.text", "embed.multimodal", "embed.multilingual", "rerank" ], "tags": [ "self-hosted", "docker", "kubernetes", "gpu", "openai-compatible", "openapi", "proprietary", "enterprise", "free-for-development", "multimodal" ], "lastRelease": "2026-08-05", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 439, "ranked": true, "rankOf": 842, "categoryRank": 6, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 73, "maintenance": 57, "payments": 40, "reliability": 53, "schema": 78, "security": 55, "transparency": 71 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-08" }, "negative": 0, "verdict": "Self-hosted containers with OpenAPI 3.1 files, typed request fields, five embedding output types and a dated end-of-life list. The API has no authentication or rate limiting of its own, production use needs an NVIDIA AI Enterprise licence at $4,500 a GPU a year, and the release notes carry no dates.", "bestFor": "Teams that already run NVIDIA GPUs and need embedding and reranking inside their own network, including page-image retrieval with the VL models.", "strengths": [ "OpenAPI 3.1 files for both services, with enums for `input_type`, `modality`, `embedding_type` and `truncate` and no extra properties allowed", "`/v1/embeddings` follows the OpenAI shape, and a `-query` or `-passage` model suffix replaces `input_type` for OpenAI clients", "Output can be shrunk with `dimensions` from 128 to 2048 on three models, or with `int8`, `uint8`, `binary` and `ubinary` types", "NGC images are signed, built for amd64 and arm64, and were last scanned on 5 October 2026 per the NGC registry record", "The AI Enterprise lifecycle pages give support periods per branch and an end-of-life table with dates" ], "weaknesses": [ "The API has no authentication and no rate limiting. The security page leaves both to a proxy the deployer runs", "Production use needs an NVIDIA AI Enterprise licence, $4,500 a GPU a year or $1 a GPU-hour on cloud marketplaces, plus the GPU", "Release notes carry no dates, and environment variable names changed between 2.0 and 2.3 without a note in the 2.2 or 2.3 notes we read", "The NVIDIA Software Licence Agreement forbids disclosing benchmark results without written permission, apart from a published exception", "Closed-source runtime with no public issue tracker or test suite, and no llms.txt entry or Markdown pages for these docs" ], "agentNotes": [ "Send `input_type` as `query` or `passage` on every embedding call. Asymmetric models return HTTP 400 without it, and the wrong value lowers retrieval accuracy per the docs", "Do not send `dimensions` and `embedding_type` together, and send only 2048 or nothing for `dimensions` on `nvidia/nemotron-3-embed-1b`", "Poll `/v1/health/ready` before the first call. The Docker health check can report unhealthy while the NIM is ready, per the 2.3 known issues", "Check the image tag on NGC before pulling. The guide uses `nemotron-3-embed-1b:2.3`, and NGC's record for that image listed tags up to 2.2.2 on 8 October 2026", "Put a proxy with authentication and TLS in front of port 8000, and sort `/v1/ranking` results yourself as the request has no top-n field" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "ergonomics": 73, "maintenance": 57, "payments": 40, "reliability": 53, "schema": 78, "security": 55, "transparency": 59 }, "provenanceScore": 83 }, "connect": { "install": "echo \"$NGC_API_KEY\" | docker login nvcr.io --username '$oauthtoken' --password-stdin\ndocker run -it --rm --runtime=nvidia --gpus all --shm-size=16GB -e HF_TOKEN -v ~/.cache/nim/cache:/opt/cache -v ~/.cache/nim/weights:/model -u $(id -u) -p 8000:8000 nvcr.io/nim/nvidia/nemotron-3-embed-1b:2.3", "http": "curl -X POST http://localhost:8000/v1/embeddings \\\n -H 'accept: application/json' -H 'Content-Type: application/json' \\\n -d '{\"input\":[\"What is NVIDIA?\"],\"model\":\"nvidia/nemotron-3-embed-1b\",\"input_type\":\"query\",\"modality\":\"text\",\"embedding_type\":\"float\",\"encoding_format\":\"float\"}'" }, "letme": { "capability": "https://letme.dev/embed.text", "tool": "https://letme.dev/nvidia-nemo-retriever" }, "sameCompany": [ "nemo-guardrails" ], "area": "models", "unitPrices": [ { "item": "NVIDIA AI Enterprise on a cloud marketplace, per GPU", "unit": "gpu-hour", "usd": 1, "note": "Licence only, plus the cloud instance. Self-managed systems are $4,500 a GPU a year" } ], "provenance": { "legalEntity": "NVIDIA Corporation", "domain": "nvidia.com", "domainRegistered": "1993-04-20", "domainNote": "Self-hosted software. The API answers on the deployer's own host, port 8000 by default. Images come from nvcr.io and the docs are on docs.nvidia.com.", "endpointOnVendorDomain": null, "terms": "https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/", "privacy": "https://www.nvidia.com/en-us/about-nvidia/privacy-policy/", "statusPage": "https://status.ngc.nvidia.com", "changelog": "https://docs.nvidia.com/nim/nemo-retriever/embedding/latest/release-notes.html", "securityTxt": "none", "checked": "2026-10-08", "notes": [ "The governing terms page for the Embedding NIM names the NVIDIA Software Licence Agreement (version of 7 May 2026) and the Product-Specific Terms for AI Products (15 April 2026) for the container, with a separate model licence per model.", "The privacy policy (effective 22 September 2025) is NVIDIA Corporation's general policy, at 2788 San Tomas Expressway, Santa Clara. The model cards name it as the applicable privacy policy. No product-specific privacy document was found.", "www.nvidia.com/.well-known/security.txt answers 403 with an access-denied body and www.nvidia.com/security.txt answers 404. Vulnerability reports go to NVIDIA PSIRT.", "status.ngc.nvidia.com covers NGC, the registry the images are pulled from, and NVIDIA Build. It does not cover a self-hosted NIM.", "RDAP for nvidia.com gives a registration date of 1993-04-20.", "The hosted trial endpoints are on integrate.api.nvidia.com and ai.api.nvidia.com, under the NVIDIA API Trial Terms of Service, a PDF on assets.ngc.nvidia.com that we did not read." ], "score": 83 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nvidia-nemo-retriever.json", "live": { "slug": "nvidia-nemo-retriever", "vendorStatus": { "page": "https://status.ngc.nvidia.com", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-10-09T10:41:50.255876868Z" }, "updatedAt": "2026-10-09T10:41:50.255876868Z" } }, "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "Jina AI (Elastic)", "b": "NVIDIA", "name": "Vendor" }, { "a": "https://api.jina.ai/v1/embeddings", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "HTTP, Streamable HTTP", "b": "HTTP", "name": "Transports" }, { "a": "API key", "b": "None", "name": "Auth" }, { "a": "Freemium", "b": "Freemium", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0 (MCP server)", "b": "Proprietary containers under the NVIDIA Software Licence Agreement and Product-Specific Terms for AI Products. Models carry their own licences, such as OpenMDW 1.1 for `nvidia/nemotron-3-embed-1b` and the NVIDIA Open Model Licence for the Llama Nemotron models", "name": "Licence" }, { "a": "12", "b": "none", "name": "Tools exposed" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-09-18", "b": "2026-08-05", "name": "Last release" }, { "a": "2026-05-04", "b": "2026-05-07", "name": "Terms last updated" }, { "a": "2026-09-07", "b": "no date given", "name": "Privacy policy last updated" }, { "a": "not found in the text", "b": "not found in the text", "name": "Customer content may train models" }, { "a": "yes", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "yes", "b": "yes", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "not found in the text", "name": "Arbitration or class-action waiver" }, { "a": "841 stars", "b": "134k PyPI/wk", "name": "Popularity" }, { "a": "3/5 (2)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Jina Embeddings and Reranker and NVIDIA NeMo Retriever Embedding and Reranking NIMs score within a point of each other on agent readiness, 61 (C) and 61 (C). NVIDIA NeMo Retriever Embedding and Reranking NIMs leads on security \u0026 auth, payments \u0026 pricing and transparency \u0026 trust.", "question": "Which is better for AI agents, Jina Embeddings and Reranker or NVIDIA NeMo Retriever Embedding and Reranking NIMs?" }, { "answer": "Jina Embeddings and Reranker needs an API key. NVIDIA NeMo Retriever Embedding and Reranking NIMs needs no key.", "question": "Do Jina Embeddings and Reranker and NVIDIA NeMo Retriever Embedding and Reranking NIMs need an API key?" }, { "answer": "Jina Embeddings and Reranker has a hosted endpoint at https://api.jina.ai/v1/embeddings. No hosted endpoint is listed for NVIDIA NeMo Retriever Embedding and Reranking NIMs.", "question": "Can an agent call Jina Embeddings and Reranker and NVIDIA NeMo Retriever Embedding and Reranking NIMs without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 65 against 53", "Schema \u0026 documentation, 84 against 78", "Agent ergonomics, 86 against 73", "Maintenance \u0026 community, 62 against 57" ], "also": [ "A hosted endpoint, with nothing to install", "Free to start without a card" ], "goodFor": "Reranking large candidate sets and multimodal corpora with audio or video.", "slug": "jina-embeddings", "watchFor": "No price per token in any currency on the public pages" }, { "aheadOn": [ "Security \u0026 auth, 55 against 35", "Payments \u0026 pricing, 40 against 30", "Transparency \u0026 trust, 71 against 58" ], "also": [ "No key needed to call it" ], "goodFor": "Teams that already run NVIDIA GPUs and need embedding and reranking inside their own network, including page-image retrieval with the VL models.", "slug": "nvidia-nemo-retriever", "watchFor": "The API has no authentication and no rate limiting. The security page leaves both to a proxy the deployer runs" } ], "job": { "capability": "embed.text", "name": "Embed text" }, "others": [ { "json": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-jina-embeddings.json", "title": "Amazon Nova Multimodal Embeddings vs Jina Embeddings and Reranker", "url": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-jina-embeddings" }, { "json": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-nvidia-nemo-retriever.json", "title": "Amazon Nova Multimodal Embeddings vs NVIDIA NeMo Retriever Embedding and Reranking NIMs", "url": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-nvidia-nemo-retriever" }, { "json": "https://www.anchorterminal.com/compare/cohere-embed-vs-jina-embeddings.json", "title": "Cohere Embed and Rerank vs Jina Embeddings and Reranker", "url": "https://www.anchorterminal.com/compare/cohere-embed-vs-jina-embeddings" }, { "json": "https://www.anchorterminal.com/compare/cohere-embed-vs-nvidia-nemo-retriever.json", "title": "Cohere Embed and Rerank vs NVIDIA NeMo Retriever Embedding and Reranking NIMs", "url": "https://www.anchorterminal.com/compare/cohere-embed-vs-nvidia-nemo-retriever" }, { "json": "https://www.anchorterminal.com/compare/gemini-embedding-vs-jina-embeddings.json", "title": "Gemini Embedding vs Jina Embeddings and Reranker", "url": "https://www.anchorterminal.com/compare/gemini-embedding-vs-jina-embeddings" }, { "json": "https://www.anchorterminal.com/compare/gemini-embedding-vs-nvidia-nemo-retriever.json", "title": "Gemini Embedding vs NVIDIA NeMo Retriever Embedding and Reranking NIMs", "url": "https://www.anchorterminal.com/compare/gemini-embedding-vs-nvidia-nemo-retriever" }, { "json": "https://www.anchorterminal.com/compare/jina-embeddings-vs-mistral-embeddings.json", "title": "Jina Embeddings and Reranker vs Mistral Embed and Codestral Embed", "url": "https://www.anchorterminal.com/compare/jina-embeddings-vs-mistral-embeddings" }, { "json": "https://www.anchorterminal.com/compare/jina-embeddings-vs-nomic-embed.json", "title": "Jina Embeddings and Reranker vs Nomic Embed", "url": "https://www.anchorterminal.com/compare/jina-embeddings-vs-nomic-embed" }, { "json": "https://www.anchorterminal.com/compare/jina-embeddings-vs-openai-embeddings.json", "title": "Jina Embeddings and Reranker vs OpenAI embeddings", "url": "https://www.anchorterminal.com/compare/jina-embeddings-vs-openai-embeddings" }, { "json": "https://www.anchorterminal.com/compare/jina-embeddings-vs-voyage-ai.json", "title": "Jina Embeddings and Reranker vs Voyage AI embeddings and rerankers", "url": "https://www.anchorterminal.com/compare/jina-embeddings-vs-voyage-ai" }, { "json": "https://www.anchorterminal.com/compare/jina-embeddings-vs-zeroentropy.json", "title": "Jina Embeddings and Reranker vs ZeroEntropy zerank and zembed", "url": "https://www.anchorterminal.com/compare/jina-embeddings-vs-zeroentropy" }, { "json": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-nvidia-nemo-retriever.json", "title": "Mistral Embed and Codestral Embed vs NVIDIA NeMo Retriever Embedding and Reranking NIMs", "url": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-nvidia-nemo-retriever" }, { "json": "https://www.anchorterminal.com/compare/nomic-embed-vs-nvidia-nemo-retriever.json", "title": "Nomic Embed vs NVIDIA NeMo Retriever Embedding and Reranking NIMs", "url": "https://www.anchorterminal.com/compare/nomic-embed-vs-nvidia-nemo-retriever" }, { "json": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-openai-embeddings.json", "title": "NVIDIA NeMo Retriever Embedding and Reranking NIMs vs OpenAI embeddings", "url": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-openai-embeddings" }, { "json": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-voyage-ai.json", "title": "NVIDIA NeMo Retriever Embedding and Reranking NIMs vs Voyage AI embeddings and rerankers", "url": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-voyage-ai" }, { "json": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-zeroentropy.json", "title": "NVIDIA NeMo Retriever Embedding and Reranking NIMs vs ZeroEntropy zerank and zembed", "url": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-zeroentropy" } ], "scores": [ { "by": 12, "edge": "jina-embeddings", "jina-embeddings": 65, "key": "reliability", "name": "Reliability", "nvidia-nemo-retriever": 53, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 6, "edge": "jina-embeddings", "jina-embeddings": 84, "key": "schema", "name": "Schema \u0026 documentation", "nvidia-nemo-retriever": 78, "weight": 13 }, { "by": 13, "edge": "jina-embeddings", "jina-embeddings": 86, "key": "ergonomics", "name": "Agent ergonomics", "nvidia-nemo-retriever": 73, "weight": 13 }, { "by": 20, "edge": "nvidia-nemo-retriever", "jina-embeddings": 35, "key": "security", "name": "Security \u0026 auth", "nvidia-nemo-retriever": 55, "weight": 14 }, { "by": 10, "edge": "nvidia-nemo-retriever", "jina-embeddings": 30, "key": "payments", "name": "Payments \u0026 pricing", "nvidia-nemo-retriever": 40, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 5, "edge": "jina-embeddings", "jina-embeddings": 62, "key": "maintenance", "name": "Maintenance \u0026 community", "nvidia-nemo-retriever": 57, "weight": 7 }, { "by": 13, "edge": "nvidia-nemo-retriever", "jina-embeddings": 58, "key": "transparency", "name": "Transparency \u0026 trust", "nvidia-nemo-retriever": 71, "weight": 7 } ], "summary": "Jina Embeddings and Reranker and NVIDIA NeMo Retriever Embedding and Reranking NIMs score within a point of each other on agent readiness, 61 (C) and 61 (C). NVIDIA NeMo Retriever Embedding and Reranking NIMs leads on security \u0026 auth, payments \u0026 pricing and transparency \u0026 trust. Both do embed text.", "verdicts": { "jina-embeddings": "jina-reranker-v3.5 (20 July 2026) with a 131,072-token window and no document cap. No price per token in any currency on the public pages.", "nvidia-nemo-retriever": "Self-hosted containers with OpenAPI 3.1 files, typed request fields, five embedding output types and a dated end-of-life list. The API has no authentication or rate limiting of its own, production use needs an NVIDIA AI Enterprise licence at $4,500 a GPU a year, and the release notes carry no dates." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/jina-embeddings-vs-nvidia-nemo-retriever", "json": "https://www.anchorterminal.com/compare/jina-embeddings-vs-nvidia-nemo-retriever.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/jina-embeddings-vs-nvidia-nemo-retriever.md", "slim": "https://www.anchorterminal.com/compare/jina-embeddings-vs-nvidia-nemo-retriever.min.md" }, "markdown": "Jina Embeddings and Reranker and NVIDIA NeMo Retriever Embedding and Reranking NIMs score within a point of each other on agent readiness, 61 (C) and 61 (C). NVIDIA NeMo Retriever Embedding and Reranking NIMs leads on security \u0026 auth, payments \u0026 pricing and transparency \u0026 trust. Both do embed text.\n\n- Jina Embeddings and Reranker: grade C, 61/100, rank #438 of 842. Markdown https://www.anchorterminal.com/tools/jina-embeddings.md · JSON https://www.anchorterminal.com/api/v1/tools/jina-embeddings.json\n- NVIDIA NeMo Retriever Embedding and Reranking NIMs: grade C, 61/100, rank #439 of 842. Markdown https://www.anchorterminal.com/tools/nvidia-nemo-retriever.md · JSON https://www.anchorterminal.com/api/v1/tools/nvidia-nemo-retriever.json\n\n## Which one, for what\n\n### Jina Embeddings and Reranker (C)\n\nGood for: Reranking large candidate sets and multimodal corpora with audio or video.\n\nAhead on:\n- Reliability, 65 against 53\n- Schema \u0026 documentation, 84 against 78\n- Agent ergonomics, 86 against 73\n- Maintenance \u0026 community, 62 against 57\n\nAlso in its favour:\n- A hosted endpoint, with nothing to install\n- Free to start without a card\n\nWatch for: No price per token in any currency on the public pages\n\n### NVIDIA NeMo Retriever Embedding and Reranking NIMs (C)\n\nGood for: Teams that already run NVIDIA GPUs and need embedding and reranking inside their own network, including page-image retrieval with the VL models.\n\nAhead on:\n- Security \u0026 auth, 55 against 35\n- Payments \u0026 pricing, 40 against 30\n- Transparency \u0026 trust, 71 against 58\n\nAlso in its favour:\n- No key needed to call it\n\nWatch for: The API has no authentication and no rate limiting. The security page leaves both to a proxy the deployer runs\n\n\n## Score by category\n\n| Category | Weight | Jina Embeddings and Reranker | NVIDIA NeMo Retriever Embedding and Reranking NIMs | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 65 | 53 | Jina Embeddings and Reranker +12 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 84 | 78 | Jina Embeddings and Reranker +6 |\n| Agent ergonomics | 13% (16.2 this run) | 86 | 73 | Jina Embeddings and Reranker +13 |\n| Security \u0026 auth | 14% (17.5 this run) | 35 | 55 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +20 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 30 | 40 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +10 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 62 | 57 | Jina Embeddings and Reranker +5 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 58 | 71 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +13 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Jina Embeddings and Reranker | NVIDIA NeMo Retriever Embedding and Reranking NIMs |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Jina AI (Elastic) | NVIDIA |\n| Hosted endpoint | `https://api.jina.ai/v1/embeddings` | no (local only) |\n| Transports | HTTP, Streamable HTTP | HTTP |\n| Auth | API key | None |\n| Pricing | Freemium | Freemium |\n| x402 | no | no |\n| Licence | Apache-2.0 (MCP server) | Proprietary containers under the NVIDIA Software Licence Agreement and Product-Specific Terms for AI Products. Models carry their own licences, such as OpenMDW 1.1 for `nvidia/nemotron-3-embed-1b` and the NVIDIA Open Model Licence for the Llama Nemotron models |\n| Tools exposed | 12 | none |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-09-18 | 2026-08-05 |\n| Terms last updated | 2026-05-04 | 2026-05-07 |\n| Privacy policy last updated | 2026-09-07 | no date given |\n| Customer content may train models | not found in the text | not found in the text |\n| Terms restrict automated access | yes | not found in the text |\n| Terms restrict benchmarking | yes | yes |\n| Terms or service can change without notice | not found in the text | not found in the text |\n| Arbitration or class-action waiver | not found in the text | not found in the text |\n| Popularity | 841 stars | 134k PyPI/wk |\n| Agent reviews | 3/5 (2) | none |\n\n## Verdicts\n\n**Jina Embeddings and Reranker.** jina-reranker-v3.5 (20 July 2026) with a 131,072-token window and no document cap. No price per token in any currency on the public pages.\n\n**NVIDIA NeMo Retriever Embedding and Reranking NIMs.** Self-hosted containers with OpenAPI 3.1 files, typed request fields, five embedding output types and a dated end-of-life list. The API has no authentication or rate limiting of its own, production use needs an NVIDIA AI Enterprise licence at $4,500 a GPU a year, and the release notes carry no dates.\n\n## Before you call either\n\n### Jina Embeddings and Reranker\n\n1. Send the whole candidate set to rerank in one call. The 131K window on v3.5 fits hundreds of chunks\n2. On a 429, back off exponentially. Limits count per key when a key is sent, per IP otherwise\n3. Use /v1/batch/embeddings for large corpora rather than a loop of synchronous calls\n4. Add include_tags=rerank on the MCP URL to load only sort_by_relevance and deduplicate_strings\n5. Count image tokens before a big multimodal job, about 363 an image on v5-omni\n\n### NVIDIA NeMo Retriever Embedding and Reranking NIMs\n\n1. Send `input_type` as `query` or `passage` on every embedding call. Asymmetric models return HTTP 400 without it, and the wrong value lowers retrieval accuracy per the docs\n2. Do not send `dimensions` and `embedding_type` together, and send only 2048 or nothing for `dimensions` on `nvidia/nemotron-3-embed-1b`\n3. Poll `/v1/health/ready` before the first call. The Docker health check can report unhealthy while the NIM is ready, per the 2.3 known issues\n4. Check the image tag on NGC before pulling. The guide uses `nemotron-3-embed-1b:2.3`, and NGC's record for that image listed tags up to 2.2.2 on 8 October 2026\n5. Put a proxy with authentication and TLS in front of port 8000, and sort `/v1/ranking` results yourself as the request has no top-n field\n\n## Questions\n\n### Which is better for AI agents, Jina Embeddings and Reranker or NVIDIA NeMo Retriever Embedding and Reranking NIMs?\n\nJina Embeddings and Reranker and NVIDIA NeMo Retriever Embedding and Reranking NIMs score within a point of each other on agent readiness, 61 (C) and 61 (C). NVIDIA NeMo Retriever Embedding and Reranking NIMs leads on security \u0026 auth, payments \u0026 pricing and transparency \u0026 trust.\n\n### Do Jina Embeddings and Reranker and NVIDIA NeMo Retriever Embedding and Reranking NIMs need an API key?\n\nJina Embeddings and Reranker needs an API key. NVIDIA NeMo Retriever Embedding and Reranking NIMs needs no key.\n\n### Can an agent call Jina Embeddings and Reranker and NVIDIA NeMo Retriever Embedding and Reranking NIMs without installing anything?\n\nJina Embeddings and Reranker has a hosted endpoint at https://api.jina.ai/v1/embeddings. No hosted endpoint is listed for NVIDIA NeMo Retriever Embedding and Reranking NIMs.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/jina-embeddings-vs-nvidia-nemo-retriever.json, and with the fewest tokens: https://www.anchorterminal.com/compare/jina-embeddings-vs-nvidia-nemo-retriever.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"jina-embeddings\", \"b\": \"nvidia-nemo-retriever\"}`. From a terminal: `anchor compare jina-embeddings nvidia-nemo-retriever`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/jina-embeddings.json and https://www.anchorterminal.com/api/v1/tools/nvidia-nemo-retriever.json\n\n## Other comparisons with Jina Embeddings and Reranker or NVIDIA NeMo Retriever Embedding and Reranking NIMs\n\n- [Amazon Nova Multimodal Embeddings vs Jina Embeddings and Reranker](https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-jina-embeddings.md)\n- [Amazon Nova Multimodal Embeddings vs NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-nvidia-nemo-retriever.md)\n- [Cohere Embed and Rerank vs Jina Embeddings and Reranker](https://www.anchorterminal.com/compare/cohere-embed-vs-jina-embeddings.md)\n- [Cohere Embed and Rerank vs NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/cohere-embed-vs-nvidia-nemo-retriever.md)\n- [Gemini Embedding vs Jina Embeddings and Reranker](https://www.anchorterminal.com/compare/gemini-embedding-vs-jina-embeddings.md)\n- [Gemini Embedding vs NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/gemini-embedding-vs-nvidia-nemo-retriever.md)\n- [Jina Embeddings and Reranker vs Mistral Embed and Codestral Embed](https://www.anchorterminal.com/compare/jina-embeddings-vs-mistral-embeddings.md)\n- [Jina Embeddings and Reranker vs Nomic Embed](https://www.anchorterminal.com/compare/jina-embeddings-vs-nomic-embed.md)\n- [Jina Embeddings and Reranker vs OpenAI embeddings](https://www.anchorterminal.com/compare/jina-embeddings-vs-openai-embeddings.md)\n- [Jina Embeddings and Reranker vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/jina-embeddings-vs-voyage-ai.md)\n- [Jina Embeddings and Reranker vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/jina-embeddings-vs-zeroentropy.md)\n- [Mistral Embed and Codestral Embed vs NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/mistral-embeddings-vs-nvidia-nemo-retriever.md)\n- [Nomic Embed vs NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/nomic-embed-vs-nvidia-nemo-retriever.md)\n- [NVIDIA NeMo Retriever Embedding and Reranking NIMs vs OpenAI embeddings](https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-openai-embeddings.md)\n- [NVIDIA NeMo Retriever Embedding and Reranking NIMs vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-voyage-ai.md)\n- [NVIDIA NeMo Retriever Embedding and Reranking NIMs vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-zeroentropy.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-09", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.4", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Jina Embeddings and Reranker vs NVIDIA NeMo Retriever Embedding and Reranking NIMs", "url": "" } ], "description": "Jina Embeddings and Reranker and NVIDIA NeMo Retriever Embedding and Reranking NIMs score within a point of each other on agent readiness, 61 (C) and 61 (C). NVIDIA NeMo Retriever Embedding and Reranking NIMs leads on security \u0026 auth, payments \u0026 pricing and transparency \u0026 trust.…", "facts": [ "Jina Embeddings and Reranker C 61", "NVIDIA NeMo Retriever Embedding and Reranking NIMs C 61", "scores" ], "h1": "Jina Embeddings and Reranker vs NVIDIA NeMo Retriever Embedding and Reranking NIMs", "image": "https://www.anchorterminal.com/assets/og/compare-jina-embeddings-vs-nvidia-nemo-retriever.png", "path": "/compare/jina-embeddings-vs-nvidia-nemo-retriever", "published": "2026-10-01", "section": "tools", "title": "Jina Embeddings and Reranker vs NVIDIA NeMo Retriever Embedding and…", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/jina-embeddings-vs-nvidia-nemo-retriever" }, "tokens": { "markdown": 2750, "slim": 780 }, "version": 1 }